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        <meta name="description" content="LREBench is a low-resource relation extraction benchmark with 8 datasets and 3 themes: prompting-based tuning, balancing methods for long-tailed distribution and leveraging unlabeled data.">
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            <h1 class="display-5 sharepoint-dark-blue">Meet Low-resource RE!</h1>
            <p class="lead sharepoint-dark-blue">An Empirical Benchmark for <b>L</b>ow-resource <b>R</b>elation <b>E</b>xtraction.</p>    
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                    <h4>Different Schemes for Low-resource Relation Extraction</h4>
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                            <center><h5 class="card-title">Standard PLM Fine-tuning</h5>
                            <p class="card-text"><font size="3rem">Supervised <b>Pre-trained Models Fine-tuning</b> with strong baselines, such as <a href="https://github.com/thunlp/OpenNRE">OpenNRE</a>.</font></p></center>
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                            <center><h5 class="card-title">Prompt-based Tuning</h5>
                            <p class="card-text"><font size="3rem">Prompting for <b>Few-shot</b> Instances with <a href="https://github.com/zjunlp/KnowPrompt">KnowPrompt</a> and <a href="https://github.com/thunlp/PTR">PTR</a>.</font></p></center>
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                            <center><h5 class="card-title">Balancing Methods</h5>
                            <p class="card-text"><font size="3rem">Re-sampling Methods and Re-weighting Losses for <br>Long-tailed Distribution Issues.</font></p></center>
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                            <center><h5 class="card-title">Leveraging More Instances</h5>
                            <p class="card-text"><font size="3rem">Diverse Data Augmentation Methods and Self-training <br>to Generate More Labeled Data <br>from Easy-collected Unlabeled Data.</font></p></center>
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                    <h4>What is Low-resource Relation Extraction Benchmark?</h4>
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                    <p>The <b>Low-resource</b> <b>Relation</b> <b>Extraciton</b> benchmark is a comprehensively empirical study on low-resource RE. It focuses on two challenges: </p>
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                        <li>Few-shot Relation Extraction</li>
                        <li>RE Data with Long-tailed Distribution</li>
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                    <p>We hope this study can help inspire future research for low-resource RE with more robust models and promote transitioning the technology to real-world industrial scenarios.</p>
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                    <h4>Paper</h4>
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                        <h6>Please cite our paper as below if you use the benchmark or codebase.</h6>
                        <pre><code>
@article{LREBench2022,
  title={Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study},
  author={Xin Xu, Xiang Chen, Ningyu Zhang, Xin Xie, Xi Chen, Huajun Chen},
  journal={EMNLP},
  year={2022}
}
                        </code></pre>
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                        <h6>Have any questions or suggestions? Feel free to reach us at <a href="https://github.com/zjunlp/LREBench" target="_blank">https://github.com/zjunlp/LREBench</a></h6>
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